Update app.py

app.py

@@ -1,48 +1,48 @@
 import streamlit as st
 from huggingface_hub import InferenceClient
 import os
-import pickle
+import sys
 
-st.title("CODEFUSSION ☄")
+st.title("CODEFUSSION ☄")  
 
 base_url = "https://api-inference.huggingface.co/models/"
+
 API_KEY = os.environ.get('HUGGINGFACE_API_KEY')
+# print(API_KEY)
+# headers = {"Authorization":"Bearer "+API_KEY}
 
 model_links = {
-    "LegacyLift🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2",
-    "ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1",
-    "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct"
+    "LegacyLift🚀": base_url + "mistralai/Mistral-7B-Instruct-v0.2",  
+    "ModernMigrate⭐": base_url + "mistralai/Mixtral-8x7B-Instruct-v0.1",  
+    "RetroRecode🔄": base_url + "microsoft/Phi-3-mini-4k-instruct" 
 }
 
+# Pull info about the model to display
 model_info = {
     "LegacyLift🚀": {
-        'description': """The LegacyLift model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \n\nThis model is best for minimal problem-solving, content writing, and daily tips.\n""",
+        'description': """The LegacyLift model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
+            \nThis model is best for minimal problem-solving, content writing, and daily tips.\n""",
         'logo': './11.jpg'
     },
+
     "ModernMigrate⭐": {
-        'description': """The ModernMigrate model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \n\nThis model excels in coding, logical reasoning, and high-speed inference. \n""",
+        'description': """The ModernMigrate model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
+            \nThis model excels in coding, logical reasoning, and high-speed inference. \n""",
         'logo': './2.jpg'
     },
+
     "RetroRecode🔄": {
-        'description': """The RetroRecode model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \n\nThis model is best suited for critical development, practical knowledge, and serverless inference.\n""",
+        'description': """The RetroRecode model is a **Large Language Model (LLM)** that's able to have question and answer interactions.\n \
+          \nThis model is best suited for critical development, practical knowledge, and serverless inference.\n""",
         'logo': './3.jpg'
     },
 }
 
-def format_prompt(message, conversation_history, custom_instructions=None):
+def format_promt(message, custom_instructions=None):
     prompt = ""
     if custom_instructions:
-        prompt += f"\[INST\] {custom_instructions} \[/INST\]"
-    
-    # Add conversation history to the prompt
-    prompt += "\[CONV_HISTORY\]\n"
-    for role, content in conversation_history:
-        prompt += f"{role.upper()}: {content}\n"
-    prompt += "\[/CONV_HISTORY\]"
-    
-    # Add the current message
-    prompt += f"\[INST\] {message} \[/INST\]"
-    
+        prompt += f"[INST] {custom_instructions} [/INST]"
+    prompt += f"[INST] {message} [/INST]"
     return prompt
 
 def reset_conversation():
@@ -51,46 +51,38 @@ def reset_conversation():
     '''
     st.session_state.conversation = []
     st.session_state.messages = []
-    save_conversation_history([])
     return None
 
-def load_conversation_history():
-    history_file = "conversation_history.pickle"
-    if os.path.exists(history_file):
-        with open(history_file, "rb") as f:
-            conversation_history = pickle.load(f)
-    else:
-        conversation_history = []
-    return conversation_history
-
-def save_conversation_history(conversation_history):
-    history_file = "conversation_history.pickle"
-    with open(history_file, "wb") as f:
-        pickle.dump(conversation_history, f)
-
 models = [key for key in model_links.keys()]
+
 selected_model = st.sidebar.selectbox("Select Model", models)
+
 temp_values = st.sidebar.slider('Select a temperature value', 0.0, 1.0, (0.5))
+
 st.sidebar.button('Reset Chat', on_click=reset_conversation)  # Reset button
 
 st.sidebar.write(f"You're now chatting with **{selected_model}**")
 st.sidebar.markdown(model_info[selected_model]['description'])
 st.sidebar.image(model_info[selected_model]['logo'])
+st.sidebar.markdown("*Generating the code might go slow if you are using low power resources *")
 
-st.sidebar.markdown("\*Generating the code might go slow if you are using low power resources \*")
 
 if "prev_option" not in st.session_state:
     st.session_state.prev_option = selected_model
 
 if st.session_state.prev_option != selected_model:
     st.session_state.messages = []
+    # st.write(f"Changed to {selected_model}")
     st.session_state.prev_option = selected_model
+    reset_conversation()
 
 repo_id = model_links[selected_model]
+
 st.subheader(f'{selected_model}')
+# st.title(f'ChatBot Using {selected_model}')
 
-# Load the conversation history from the file
-st.session_state.messages = load_conversation_history()
+if "messages" not in st.session_state:
+    st.session_state.messages = []
 
 for message in st.session_state.messages:
     with st.chat_message(message["role"]):
@@ -98,30 +90,24 @@ for message in st.session_state.messages:
 
 if prompt := st.chat_input(f"Hi I'm {selected_model}, How can I help you today?"):
     custom_instruction = "Act like a Human in conversation"
+
     with st.chat_message("user"):
         st.markdown(prompt)
-    
+
     st.session_state.messages.append({"role": "user", "content": prompt})
-    conversation_history = [(message["role"], message["content"]) for message in st.session_state.messages]
-    
-    formatted_text = format_prompt(prompt, conversation_history, custom_instruction)
-    
-    max_new_tokens = 3000
-    if selected_model != "RetroRecode🔄":
-        input_tokens = len(formatted_text.split())
-        max_tokens = {"LegacyLift🚀": 32000, "ModernMigrate⭐": 8192}
-        max_new_tokens = max_tokens[selected_model] - input_tokens
+
+    formated_text = format_promt(prompt, custom_instruction)
 
     with st.chat_message("assistant"):
-        client = InferenceClient(model=model_links[selected_model])
+        client = InferenceClient(
+            model=model_links[selected_model], )
+
         output = client.text_generation(
-            formatted_text,
-            temperature=temp_values,
-            max_new_tokens=max_new_tokens,
+            formated_text,
+            temperature=temp_values,  # 0.5
+            max_new_tokens=3000,
             stream=True
         )
+
         response = st.write_stream(output)
-        st.session_state.messages.append({"role": "assistant", "content": response})
-        
-        # Save the updated conversation history to the file
-        save_conversation_history(st.session_state.messages)
+    st.session_state.messages.append({"role": "assistant", "content": response})